Eddy covariance Totally Explained

Everything about Eddy Covariance totally explained

The Eddy Covariance (aka Eddy Correlation, Eddy Flux) technique is a prime atmospheric flux measurement technique to measure and calculate vertical turbulent fluxes within atmospheric boundary layers. It is a statistical method used in meteorology and other applications that analyzes high-frequency wind and scalar atmospheric data series, and yields values of fluxes of these properties. Such flux measurements are widely used to estimate momentum, heat, water, and carbon dioxide exchange, as well as exchange of methane and other trace gases. The technique is also used extensively for verification and tuning of global climate models, mesoscale and weather models, complex biogeochemical and ecological models, and remote sensing estimates from satellites and aircraft. The technique is mathematically complex, and requires significant care in setting up and processing data. To date, there's no uniform terminology or a single methodology for the Eddy Covariance technique, but much effort is being made by flux measurement networks (for example, Fluxnet

, Ameriflux

, CarboEurope

, Fluxnet Canada

, and iLEAPS

) to unify the various approaches.

General principles

Representation of the air flow in the atmospheric boundary layer Air flow can be imagined as a horizontal flow of numerous rotating eddies, a turbulent vortices of various sizes, with each eddy having 3D components, including vertical components as well. The situation looks chaotic, but vertical movement of the components can be measured from the tower. Physical meaning of the Eddy Covariance method At one physical point on the tower, at Time1, Eddy1 moves parcel of air c1 down at the speed w1. Then, at Time2, Eddy2 moves parcel c2 up at the speed w2. Each parcel has a concentration, temperature, and humidity. If these factors, along with the speed are known, we can determine the flux. For example, if one knew how many molecules of water went up with eddies at Time 1, and how many molecules went down with eddies at Time2, at the same point, one could calculate the vertical flux of water at this point over this time. So, vertical flux can be presented as a covariance of the vertical wind velocity and the concentration of the entity of interest.

Mathematical foundation

In mathematical terms, "eddy flux" is computed as a covariance between instantaneous deviation in vertical wind speed (w') from the mean value (w-overbar) and instantaneous deviation in gas concentration, mixing ratio (s'), from its mean value (s-overbar), multiplied by mean air density (ρa). Several mathematical operations and assumptions, including Reynolds decomposition, are involved in getting from physically complete equations of the turbulent flow to practical equations for computing "eddy flux", as shown below.

Major assumptions

Measurements at a point can represent an upwind area

Measurements are done inside the boundary layer of interest

Fetch/flux footprint is adequate – fluxes are measured only at area of interest

Flux is fully turbulent – most of the net vertical transfer is done by eddies

Terrain is horizontal and uniformed: average of fluctuations is zero; density fluctuations negligible; flow convergence & divergence negligible

Instruments can detect very small changes at high frequency, ranging from minimum of 5 Hz and to 40 Hz for tower-based measurements

Summary

In a nutshell, the 3D wind and another variable (usually CO₂ concentration, etc) are decomposed into mean and fluctuating components. The covariance is calculated between the fluctuating component of the vertical wind and the fluctuating component of CO₂ concentration. The upward flux of CO₂ is proportional to the covariance.
The area from which the detected eddies originate can be described probabilistically. Therefore, the area from which the flux of the variable is measured, is uncertain.
The effect of sensor separation, finite sampling length, sonic path averaging; as well as other instrumental limitations all lead jointly to systematic underestimation of covariance results. In order to correct for these effects spectrum-dependent correction procedures need to be applied.

Further Information

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